CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Identification and analysis of binding site residues in protein complexes: energy based approach
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Kernel methods for Calmodulin binding and binding site prediction
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Detection of Outlier Residues for Improving Interface Prediction in Protein Heterocomplexes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein--protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues. Results: Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein--protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein--protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone. Contact: yanay.ofran@columbia.edu